Lingolette: AI Language Tutor’s Post

Where we messed up 14 months ago, we brought a new technology to the market. (called 'waterfall enrichment') It was the perfect scenario: The market pull was huge. Anew method that changed access to contact data. So great, that a lot of new entrants copied the method. Which quickly made us realize: Being the first-mover can be dangerous. Why? Because you can become complacent. There is no one that challenges you. No competitor that potential customers can compare you to. And I must admit: At some point, some of our competitors had a more appealing product. That’s why we went back to 12 months ago. And created an unbeatable offer (again). We added what we got requested over the years: 1. Mobile phone number enrichment 2. Google Sheets native integration 3. Real-time catch-all verification  Did you experience something similar? 👇

Building Tools for African Businesses: My Journey with Taaskly [4] - Jun-29-2024 Another Saturday, another newsletter. LFGGG! So, what have I been up to this past week? Nothing exciting, to be honest. I am currently working on bookings, after which I will move on to the AI To-do App. I have decided to make all sub-Taaskly products open-source. Instead of having a super app that does a hundred things, why not have a hundred apps that do one thing? That’s the approach I am using. It makes development faster and marketing easier: only use what you want and nothing else. I am looking at a pay-as-you-go model, but more research is needed on that front. I won't be publishing any products today, although I might have to make a video on how Taaskly Invoice works. Maybe I will change the landing page to be more consistent with the other Taaskly pages. Monday morning update: Made the video and made adjustments to the landing page. Links: Demo: https://lnkd.in/eCP5WyDR Site: https://lnkd.in/eEmxAfBD Github: https://lnkd.in/eS5dMETC Feedback here: https://lnkd.in/e64kEYgQ

🌎 UserLoop can now automatically translate your surveys into over 100 languages. Our built-in AI not only automatically translates your entire survey, but also serves it to the customer in their preferred language. Whether you're surveying customers in your Shopify checkout with Checkout Extensibility, by email or with a link - they'll get the survey in their own language. All responses collected can be analyzed in a single language to make it easy to identify customer trends across different markets. Check out the demo of how AI-powered translation works in UserLoop! https://lnkd.in/ewZ27x99

When to use Bandits has a simple answer (“When you want to optimize copy for a short-lived promotion, like Black Friday.”) and a right answer. If you are interested in the later, Ryan Lucht and Sven Schmit have you covered.

The difference between Multi-Arm Bandit and Recommendation Systems This is inspired by my discussion with Sven Schmit in this post: https://lnkd.in/ggcan9yJ Sven's comment -- "both recommendation systems and contextual bandits can personalize experiences, so it seems reasonable to compare the two and highlight in which scenarios each of them is most appropriate." -- signals a common misconception about MAB versus recommendation systems. At first glance, MAB and recommendation systems appear to share the same goal of dynamically finding the best-performing variant to present to customers. However, the crucial difference lies in the details. MAB dynamically allocates "overall" traffic. That is, MAB treats users roughly the same, simply identifying which variant performs best "on average" and directing the optimal number of users to that arm. Recommendation systems, on the other hand, emphasize "personalization." Each user, with their features (considered as the right-hand side variables in an ML model, not product features), will undergo the ranking algorithms and be served the best choice, individually. Indeed, you can incorporate these features into MAB, which actually falls under a subset of the reinforcement learning paradigm, for more "individualized" allocation. But at that point, you shouldn't limit yourself to MAB and should instead build an optimized recommendation system based on first principles. Optimizing a ML system in production is hard. You shouldn’t impose such a structural constraint on your Machine Learning Engineers / Applied Scientists without a clear reason. As for the analogy, consider the difference between a chef's tasting menu and a bespoke meal crafted just for you. A Multi-Arm Bandit (MAB) is like a chef who prepares a tasting menu based on what dishes have been most popular with diners on average. The chef dynamically adjusts the menu to ensure the overall satisfaction of the restaurant's clientele, but each diner receives the same selection. On the other hand, a recommendation system is akin to a chef who consults with each diner individually, takes note of their preferences, dietary restrictions, and past dining experiences, and then prepares a meal tailored just for them. This system ensures that each person's meal is optimized for their personal enjoyment, much like how a recommendation system personalizes experiences by considering the unique characteristics of each user. In practice, you would seldom face the choice of MAB vs. recommendation systems, therefore my point -- you should understand the different purposes these different systems serve, before you chase some difference in irrelevant details.

The marketing or product team needs to make their user experience personalized. Should you reach for a recommendation system or a contextual bandit algorithm? I wasn't sure of the distinction either! So I bothered Sven Schmit, repeatedly, to help me learn about the ideal use cases for each and the key considerations that can help you pick the right tool for the job. Then I wrote it all up in a handy blog post for everyone else :) The tl;dr - here's what you'll need to consider: -How big is the problem at hand? Does it warrant the heft of a recommendation system, or are there fewer arms and decisions to make? -Do you have enough historical data to overcome a cold start problem? If not, you'd be best served by a contextual bandit -How many times do we need to make this decision for each user? You can read through all these distinctions (and a bunch more color) on the Eppo blog: https://lnkd.in/dUvKpdAy

Have you ever been overwhelmed by broad search results? Dive into the world of faceted search, the ultimate shortcut for both buyers and businesses, in our blog post, 'Search That Converts: A Complete Guide to Ecommerce Faceted Search.' Learn how to optimize your faceted search for more conversions and improved customer experiences with real-life examples as your guide. Plus, discover the secret sauce: AI-native product discovery that leverages advanced algorithms, transformers, and large language models (LLMs) to personalize product rankings on a user-level. Click here for insights, challenges, best practices, and actionable tips to transform your ecommerce game: https://bit.ly/3OnEiVN. 👈 #Ecommerce #AIProductDiscovery #CustomerExperience

Product update! You can now cancel messages in Zoë. Sometimes you want an LLM to wax eloquently to explain what’s going on with your marketing spend, and sometimes you just want it to stop so you can add “include no other comments” to your question. Whatever your preference, you can now cancel messages to Zoë and change your question how you like. The other update, isn’t so much an update as it is a showcase of my talk at Data Universe last week. I talked about Agents vs Chatbots. Zoë is a great example of an agent. Agents plan / Chatbots react  Agents use tools / Chatbots write text Agents iterate to find solutions / Chatbots answer questions To make that visual, take a look at Zoë using her tools to not just help you create a dashboard faster, but give you a coworker you can delegate that task to.

You know those amazing documents that you carefully created but no one uses? And the time you spend using ctrl+F to navigate on your information pool? Also, do you sometimes have to search within your email/slack to find that valuable piece of information/documents you need? That is all over. Introducing to you the Knowledge Management solution - All the answers you need, on demand.